Keynote Summaries of the First International Symposium on Dynamics, Monitoring and Diagnostics

Abstract

The first International Symposium on Dynamics, Monitoring and Diagnostics was held in Chongqing, China, in April 2022. The Symposium, which was attended both virtually and in person, had an audience of 2000 and was aimed at enhancing the intelligence of condition monitoring for engineering systems. During the Symposium, five keynote addresses were delivered by world leading experts, and this paper is comprised of summaries of these addresses to ensure that the important messages of these speakers are properly on record and readily able to be referenced.

Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing

“…High-fidelity digital models of physical entities can produce simulated data that approximate the performance of real systems. Therefore, DT is an emerging technology to address the scarcity of labeled data in real-world industrial scenarios and is gaining fast momentum in the field of machinery health management research [35]. A schematic diagram of machinery health management tasks based on the digital twin is shown in Fig 1 . DT is a virtual mirror of a physical entity along its lifecycle.…”

IFD-MDCN: Multibranch denoising convolutional networks with improved flow direction strategy for intelligent fault diagnosis of rolling bearings under noisy conditions

“…The faults and failure of rolling bearings can affect the normal operation of the equipment and even cause catastrophic accidents. To ensure the safe operation of machinery and equipment, it is necessary to implement real-time condition monitoring and fault diagnosis for rolling bearings [2,3].…”

“…Ge et al [32] magnified the feature information by a logarithmic module, and then extracted the features by using a modified self-calibrating residual network. Xu et al [2] developed a global contextual residual convolutional neural network for machine fault detection. A global context module was introduced to guide CNN to focus more on global discriminative features.…”